Synopsis

Accurate cardiac synchronization
is essential in CMR. At ultra-high field, ECG triggering can be significantly
impacted by the magneto-hydrodynamic (MHD) effect. Here, we investigate the
performance of a conventional 3-lead ECG trigger device and a state-of-the-art
trigger algorithm for cardiac ECG synchronization at 7 T. We show that by
appropriate subject preparation and by including a learning phase for the
R-wave detection outside of the magnetic field, reliable ECG triggering at
ultra-high field is feasible despite severe distortions of the ECG signal. A quantitative
analysis in 10 healthy subjects revealed a trigger sensitivity and specificity of 97.6% and
98.4%, respectively.

Background

High
spatial resolution and new opportunities for MR-based tissue characterization
or metabolic imaging have recently encouraged investigations into
ultra-high-field (B0 ≥ 7 Tesla) cardiovascular magnetic resonance
(CMR). While accurate cardiac synchronization is essential, ECG triggering at
ultra-high field can be significantly impacted by the magneto-hydrodynamic
(MHD) effect. Blood flow within the
static magnetic field induces a voltage, which superimposes the ECG signal and
can affect the recognition of the R-wave. Scaling with B0, the
effect is particularly pronounced at ultra-high field (1-3).

The purpose of this study was to fully explore
the technical capabilities of a 7 T research MR scanner and state-of-the-art
3-lead ECG equipment for cardiac synchronization at ultra-high field. We
demonstrate that
by including an appropriate ECG learning phase outside of the magnetic field,
existing ECG trigger technology in 7 T research systems allows for generating
stable and reliable ECG trigger signals in healthy volunteers.

To ensure accurate triggering, ECG electrodes were placed following the
manufacturer’s instructions,
and in conjunction with a senior electrophysiology cardiac scientist. The
trigger algorithm was calibrated by observing the subject’s ECG outside the magnet for
approximately 30s (1,6). This learning phase allows the trigger algorithm to store
the shape of the rising edge of the R-wave without the presence of the MHD
effect. Once learning is completed, the trigger algorithm continuously compares
the incoming ECG signal to the learned shape in real-time and initiates trigger
events based on several conditions (4).

To obtain a quantitative estimate, false negative (unidentified
R-wave) and false positive (trigger not identifying an R-wave) trigger events
were identified manually in the ECG recordings. From each subject, a
representative continuous section of the ECG signal containing up to 500
trigger events was included in the evaluation. From the results, sensitivity
and specificity were calculated as follows:

Sensitivity
= (NRR – NFN) / NRR,

Specificity
= (NRR – NFP) / NRR,

with NRR, NFP and NFP
denoting the number of RR-intervals, false negatives and false positives,
respectively.

Results

A
representative section of the recorded ECG signal time curves is shown in Fig. 1a.
Despite severe MHD-based signal distortions, trigger events are typically
initiated accurately. Increased distortions are particularly visible during
periods of enhanced breathing in preparation for a breath-held scan (Fig. 1b).
Examples of typical vectorcardiograms are given in Fig. 2. Only
little variation is observed in the location of the trigger events, indicating
a high trigger fidelity. Imaging gradients did not significantly impact the ECG signal (6). For the initial quantitative evaluation in 10
volunteers, an overall of 4634 R-waves were investigated, yielding 113 false
negative (sensitivity = 97.6%) and 76 false positive (specificity: 98.4%)
events. For the vast majority of cases, the reconstructed images were free of
visible trigger-related artefacts. Examples are given in Fig. 3.

Conclusion

Our
results indicate that reliable cardiac ECG triggering is feasible in healthy
volunteers at ultra-high field utilizing a state-of-the-art 3-lead trigger
device. By means of accurate subject preparation and by including a learning
phase outside of the magnetic field, the employed trigger algorithm provided
sufficient accuracy for high-fidelity CMR despite severe ECG signal distortions
by the MHD effect. Future work will need to evaluate the synchronization setup
in larger cohorts and patients with cardiac arrhythmia. Apart from CMR, also other
ultra-high-field imaging applications such as human brain functional MRI with
physiologic noise correction may benefit from robust ECG triggering and the
easy instrumentational setup.

Acknowledgements

MB
acknowledges funding from ARC Future Fellowship grant FT140100865. The authors
acknowledge the facilities of the National Imaging Facility at the Centre for
Advanced Imaging, University of Queensland.

Figures

Figure 1: Representative traces, 1st
(red) and 2nd (blue) channel, of the ECG signal over time measured
in one volunteer at the magnet isocenter during normal (a) and enhanced (b)
breathing. Trigger events (green) are regularly spaced and correspond to time
points at which the learnt and actual rising edge of the R-wave match
sufficiently well. The black arrow marks the start of an image acquisition.

Figure 2: Vectorcardiograms obtained in
2 healthy subjects over a period of 20 seconds. The trigger events (dark
circles) generally occur at a similar location in this vector space.